{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "datasets.make_*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.datasets as d\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg_data = d.make_regression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000L, 10L)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complex_reg_data = d.make_regression(1000, 10, 5, 2, 1.0)\n",
    "complex_reg_data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classification_set = d.make_classification(weights=[0.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 90], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(classification_set[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "blobs_data, blobs_target = d.make_blobs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9fa0b38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4e/fu5ciRI9SsWfO2ujIyMjIICipHQsL9QHWc/fLTiIraSkhICFprrFbrpaGLCQkJdO7c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\nEB5GErsQQniY/wcBwdmbCswzxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9dcfc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline     \n",
    "plt.scatter(blobs_data[:,0],blobs_data[:,1],c = blobs_target) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
